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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Dan, Zou Peiran, Wang Jiang, Wu Siqi, Sun Li, Guohua Ma, Xiaoning Railway Big Data Research and Application Innovation Center China Academy of Railway Sciences Beijing100081 China

ISBN: (纸本)9789819639687

This paper explores the background of constructing a railway data lake and presents a comprehensive framework for its architecture. The core components of this framework include three key elements: data ingestion standards, data ingestion strategies, and data storage. The study investigates two methods of data ingestion: physical ingestion and logical ingestion. Physical ingestion is particularly suitable for scenarios involving smaller data volumes and lower real-time requirements, while logical ingestion is designed for handling large datasets with high real-time demands and elevated security *** paper clearly defines six standards for data ingestion and outlines the specific workflows associated with each of the two ingestion methods. Establishing a railway data lake plays a crucial role in addressing issues such as data silos and weak data governance. Furthermore, the implementation process of the data lake is designed to integrate various technologies and management strategies, thereby enhancing the overall optimization of railway data *** creating a data lake, the railway sector can not only improve data accessibility and interoperability but also foster a data-driven culture that promotes informed decision-making. This initiative is expected to streamline operations, enhance data quality, and ultimately contribute to the efficiency and safety of railway systems. The insights gained from this research provide a valuable reference for the future development of data management frameworks within the railway industry and other sectors facing similar challenges. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Information management

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A Cross-System Dynamic data Fusion Method Based on Train Operation Trajectories

A Cross-System Dynamic Data Fusion Method Based on Train Ope...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zou, Dan Sun, Siqi Wang, Peiran Wu, Jiang China Academy of Railway Sciences Railway Big Data Research and Application Innovation Center Beijing100081 China

ISBN: (纸本)9789819639687

As railway transportation systems become increasingly complex, the demand for cross-system data fusion continues to grow. This paper proposes a cross-system dynamic data fusion method based on train operation trajectories, aimed at enhancing the integration and analytical capabilities of data within the railway industry. First, we provide an overview of the current state of research on data fusion, highlighting the limitations of existing methods and laying the groundwork for this study. Next, we clarify the objectives of data fusion and the scope of the data involved, focusing on the core elements of spatiotemporal fusion based on train operation trajectories. Building on this foundation, we construct a knowledge graph centered on these core fusion elements to effectively link and organize data from various systems. Through this method, we generate a series of data fusion results that offer deeper business insights. Additionally, the practical effectiveness and value of this method are demonstrated through specific case studies. Finally, we summarize the main findings of the research and emphasize the potential of this method in promoting data-driven decision-making in the railway industry, providing a reference for future research and practice. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： data fusion

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railway Multi-source data Analysis and Fusion Architecture research

Railway Multi-source Data Analysis and Fusion Architecture R...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zou, Dan Sun, Siqi Wang, Peiran Wu, Jiang Railway Big Data Research and Application Innovation Center China Academy of Railway Sciences Beijing100081 China

ISBN: (纸本)9789819639687

In order to facilitate large-scale data analysis and application within the railway industry, the central integration of data from various business departments across the network is imperative. This integration process encounters numerous challenges, as the complex multi-source data derived from diverse business units and systems presents significant issues related to data complexity and heterogeneity. These challenges underscore the urgent necessity for a well-structured and effective data fusion architecture, which plays a critical role in the handling and integration of such data. This paper presents a comprehensive study and design of a multi-source data fusion architecture tailored for the railway sector, addressing the specific challenges encountered during the data fusion process. The research emphasizes the key stages of the fusion process, including data processing, fusion levels, and fusion algorithms, thereby providing a structured framework for data integration. Moreover, the study outlines the functional requirements for platform tools and software that are essential for facilitating data integration, analysis, and sharing within the railway industry. By providing a robust architecture and detailing the necessary functionalities, this paper aims to enhance the efficiency and effectiveness of data utilization in railway operations, ultimately supporting informed decision-making and operational excellence. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： data assimilation

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Efect of Pre‑stretch Strain at High Temperatures on the Formability of AZ31 Magnesium Alloy Sheets

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Acta Metallurgica Sinica(English Letters) 2023年第1期36卷 48-60页

作者： Yongqiao Li Lifei Wang Xiaohuan Pan Qiang Zhang Guangsheng Huang Bin Xing Weili Cheng Hongxia Wang Kwang Seon Shin College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China Chongqing Innovation Center of Industrial Big-Data Co.Ltd. National Engineering Laboratory for Industrial Big-Data Application TechnologyChongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center for Magnesium Alloys Chongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National UniversitySeoul 881416Republic of Korea

High-temperature pre-stretching experiments were carried out on the AZ31 Mg alloy at 723 K with strain levels of 2.54%,6.48%,10.92%,and 19.2%to alter the microstructure and texture for improving room-temperature *** results showed that the strain-hardening coefcient increased,while the Lankford value *** addition,the Erichsen values of all pre-stretch sheets were enhanced compared with that of the as-received *** maximum Erichsen value increased from 2.38 mm for the as-received sample to 4.03 mm for the 10.92%-stretched sample,corresponding to an improvement of 69.32%.This improvement was mainly attributed to the gradual increase in grain size,and the(0001)basal texture was weakened due to the activated non-basal slip as the high-temperature pre-stretching strain levels *** visco-plastic self-consistent analysis was performed on the as-received and high-temperature pre-stretched *** confrmed the higher activity of the prismatic slip in 10.92%-stretched sample,leading to divergence and weakening of basal texture *** results in an augmentation of the Schmid factor under diferent slip ***,it can be concluded that high-temperature pre-stretching technology provided an efective method to enhance the formability of Mg alloy sheets.

关键词： High-temperature deformation Grain growth Magnesium alloys Texture Visco-plastic self-consistent modeling Formability

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Hotspots and frontiers of the relationship between gastric cancer and depression:A bibliometric study

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World Journal of Gastroenterology 2023年第46期29卷 6076-6088页

作者： Jia-Yu Liu Ji-Qi Zheng Cheng-Liang Yin Wen-Pei Tang Jian-Ning Zhang Department of Neurosurgery The First Medical CentreChinese PLA General HospitalBeijing 100853China School of Health Humanities Peking UniversityBeijing 100191China Medical Big Data Research Center Medical Innovation Research Division of Chinese PLA General HospitalBeijing 100853China National Engineering Research Center for Medical Big Data Application Technology Chinese PLA General HospitalBeijing 100853China

BACKGROUND A significant relationship between gastric cancer(GC)and depression has been found in the last 20 ***,there is no comprehensive information that helps researchers find popular and potential research directions on GC and *** To determine the research status and hotspots by bibliometric analysis of relevant publications on the relationship between GC and *** We used the Web of Science Core Collection to search and collate the literature on GC and depression from 2000 to 2022 on 31 May,***,visualization analysis was performed using VOSviewer software(version 1.6.19)and the Bibliometrix package in R *** We retrieved 153 pertinent publications from 2000 to *** annual publication count showed an overall upward *** had the most prominent publications and significant contributions to this field(n=64,41.83%).Before 2020,most studies focused on“the effect of GC on the development and progression of depression in patients.”The latest research trends indicate that“the effect of depression on the occurrence and development of GC and its mechanism”will receive more attention in the *** The study of“the effect of depression on the occurrence and development of GC and its mechanism”has emerged as a novel research theme over the past two years,which may become a research hotspot in this *** study provides new insights into the hotpots and frontiers of the relationship between GC and depression,potentially guiding researchers toward hot research topics in the future.

关键词： Gastric cancer Depression Bibliometric analysis Visualization Web of Science

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research on Tensor Multi-Clustering Distributed Incremental Updating Method for big data

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Computers, Materials & Continua 2024年第10期81卷 1409-1432页

作者： Hongjun Zhang Zeyu Zhang Yilong Ruan Hao Ye Peng Li Desheng Shi School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China Ministry of Science and Technology Innovation China Communications Services Corporation LimitedBeijing100071China School of Artificial Intelligence The University of ManchesterManchesterM139PLUK Ministry of Technology Innovation China Telecom Artificial Intelligence Technology(Beijing)Corporation LimitedBeijing100032China Ministry of Science and Technology Innovation Zhongbo Information Technology Research Institute Corporation LimitedNanjing210012China Jiangsu Postal Big Data Technology and Application Engineering Research Center Nanjing University of Posts and TelecommunicationsNanjing210003China

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering *** address this issue,this paper introduces a new method named big data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering ***,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing ***,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary *** new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in ***,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic *** experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most ***,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering *** not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through *** method shows great potential in real-world applications where dynamic data growth is common,and it is of significant imp

关键词： Tensor incremental update distributed clustering processing big data

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Unveiling the planar deformation mechanisms for improved formability in pre-twinned AZ31 Mg alloy sheet at warm temperature

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Journal of Magnesium and Alloys 2023年第12期11卷 4659-4678页

作者： Xiaohuan Pan Lifei Wang Pengbin Lu Hua Zhang Guangsheng Huang Liuwei Zheng Bin Xing Weili Cheng Hongxia Wang Wei Liang Kwang Seon Shin Shanxi Key Laboratory of Advanced Magnesium-Based Materials College of Materials Science and EngineeringTaiyuan University of TechnologyTaiyuan 030024China National Engineering Laboratory for Industrial Big-data Application Technology Chongqing Innovation Center of Industrial Big-Data Co.Ltd.Chongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute for Advanced Studies in Precision Materials Yantai UniversityYantai 264005China National Engineering Research Center for Magnesium Alloys College of Materials Science&EngineeringChongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National University1 Gwanak-roGwanak-guSeoul 08826South Korea

To investigate the role of pre-twins in Mg alloy sheets during warm planar deformation,the stretch forming is conducted at 200℃.Results suggest the formability of the pre-twinned AZ31 Mg alloy sheet is enhanced to 11.30 *** mechanisms for the improved formability and the deformation behaviors during the planar stretch forming are systematically investigated based on the planar stress *** Schmid factor for deformation mechanisms are calculated,the results reveal that planar stress states extremely affect the Schmid factor for{10-12}*** detwinning is activated and the prismatic slip is enhanced in the pre-twinned sheet,especially under the planar extension stress state in the outer ***,the thickness-direction strain is accommodated *** dynamic recrystallization(DRX)type is continuous DRX(CDRX)regardless of the planar stress ***,the CDRX degree is greater under the planar extension stress *** twin lattices deviate from the perfect{10-12}twinning relation due to the planar compression stress state and the *** basal texture is weakened when the planar stress state tends to change the texture components.

关键词： AZ31 Mg alloy sheet Planar stretch forming Planar stress states Continuous dynamic recrystallization {10-12}tensile twinning

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Progress and prospects of data-driven stock price forecasting research

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International Journal of Cognitive Computing in Engineering 2023年第1期4卷 100-108页

作者： Zhao, Chuanjun Wu, Meiling Liu, Jingfeng Duan, Zening li, Jie Shen, Lihua Shangguan, Xuekui Liu, Donghang Wang, Yanjie School of Information Shanxi University of Finance and Economics Shanxi Taiyuan030006 China School of Journalism and Mass Communication University of Wisconsin-Madison MadisonWI53706 United States Financial Innovation and Big Data Statistical Analysis Laboratory Shanxi University of Finance and Economics Shanxi Taiyuan030006 China Shanxi Information Technology Application Innovation Engineering Research Center Shanxi Taiyuan030006 China

With the rapid development of social economy and the continuous improvement of stock market, stock investment has become more and more widely concerned. Stock price prediction has become an important research direction in the field of cognitive computing in engineering. data-driven stock price forecasting aims to predict future stock price trends based on historical values and textual data, which can effectively help people reduce risks and improve returns in the process of stock investment. The article reviews the literature on stock price forecasting methods, and classifies stock price forecasting methods from two different perspectives of model and feature. According to different model angles, the existing stock price prediction methods can be divided into statistical analysis methods, traditional machine learning methods and deep learning methods. According to different characteristic angles, the existing stock price prediction methods can be divided into those based on numerical data and those based on text mixed with numerical data. Finally, we summarize the research challenges faced by stock price prediction and provide future research directions. © 2023

关键词： Investments

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A Lightweight CNN-Transformer Network with Agent Attention Mechanism for 3D Whole Heart Segmentation 7

A Lightweight CNN-Transformer Network with Agent Attention M...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Li, Jiatong Wang, Yifan Du, Dianrong Cui, Hengfei Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China Chongqing Innovation Center Northwestern Polytechnical University Chongqing China

ISBN: (纸本)9798350350890

In recent years, heart diseases have become one of the major global health issues, emphasizing the importance of 3D whole heart segmentation technology for accurate and efficient treatment. Currently, 3D whole heart segmentation algorithms often combine Transformer technologies with 3D Convolutional Neural Networks (3D CNNs), leveraging the self-attention mechanism of Transformers for global information modeling and 3D CNNs for capturing local contextual information. However, the high computational complexity of Transformers leads to huge computational resource wastage. To address this issue, this work proposes a novel whole heart segmentation algorithm named AgentTransBTS, based on an improved 3D Agent Attention mechanism and the TransBTS framework. This algorithm utilizes TransBTS as the baseline model, capitalizing on its deep feature extraction and information integration capabilities to capture global dependencies in images. To reduce computational complexity without sacrificing the model's expressive power, an enhanced 3D Agent Attention mechanism is introduced. Comprehensive experiments were conducted on the Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 dataset to evaluate the proposed algorithm. The experimental results demonstrate that our proposed method achieved an average Dice coefficient of 91.9% on the CT dataset and 79.9% on the MRI dataset of the MM-WHS 2017 challenge. Furthermore, our algorithm's FLOPs are 217G, representing an approximate 9.2% improvement in computational efficiency compared to the baseline network. © 2024 IEEE.

关键词： Convolutional neural networks

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